Stochastic Sparse Tree Grids for Spatial Filtering Deinterleaving
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Solution Overview
Problem
Existing spatial filtering systems face challenges in efficiently deinterleaving signal parameter vector data, particularly with new characteristics and non-standard data, requiring complex processor architectures and struggling to provide accurate spatial location information, especially when using stochastic histogram methods with small cells.
Innovation Solution
The integration of functional grid elements into stochastic sparse tree grids for spatial filtering enables efficient deinterleaving of signal parameter vector data, allowing for high-performance processing of both standard and new data using a single platform, including the combination of non-standard signal parameters and moving emitter platform data, and generating accurate results from AOA data with standard processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stochastic histogram methods are used with very small cells to spread out spatial data for accurate results, then measurement precision is improved, but productivity deteriorates due to unacceptably inefficient use of standard post-processing architecture
Solution Approach 1:
The patent divides the spatial filtering process into two distinct stages: a pre-processing stage that generates enhanced signal parameter vectors with new characteristics, and a post-processing stage that uses specialized architectures (spherical harmonic decomposition units, deinterleavers) to efficiently handle the enriched data. This segmentation allows each stage to be optimized independently, maintaining high precision while improving overall processing efficiency.
Solution Approach 2:
The patent introduces intermediate data structures and processing units that act as mediators between the pre-processing and final output stages. These include spherical harmonic decomposition units that transform signal data into intermediate representations, and deinterleavers that reorganize the enriched signal parameter vectors. These intermediaries enable standard post-processing architecture to handle complex spatial data efficiently without sacrificing accuracy.
2Measurement precision
If improved pre-processing front-end architectures are used to generate signal data vectors with new characteristics, then measurement precision is improved, but device complexity worsens due to requirement for more extensive processing systems
Solution Approach 1:
The patent designs post-processing units with multi-functional capabilities that can handle various types of enriched signal parameter vectors generated by different pre-processing methods. The spherical harmonic decomposition units and deinterleavers are configured to process multiple data formats and characteristics uniformly, reducing the need for separate specialized hardware for each pre-processing technique and thereby controlling overall system complexity.
3Measurement precision
If denoising and blind source separation are applied to generate signal parameter vectors with new characteristics and additional information, then measurement precision is improved, but device complexity worsens due to requirement for substantially more complex processor architectures
Solution Approach 1:
The patent transforms the processing approach by moving from traditional spatial domain filtering to spherical harmonic decomposition, which operates in a different mathematical dimension. This dimensional transformation allows the system to handle enriched signal parameter vectors with new characteristics more efficiently, as the spherical harmonic basis functions naturally capture the spatial correlations in the data without requiring proportionally increased hardware complexity.
Data Source
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AI summary
A method of spatially filtering signal parameter vector data includes receiving, at a computing device, a first signal parameter vector at a first time and a second signal parameter vector at a second time occurring after the first time. The first and second signal parameter vectors are derived from a plurality of signals received at a sensor, and include first and second signal data blocks, respectively. The method also includes transmitting, to at least a first and second element of an array data structure representative of a physical spatial domain, the first and second signal data blocks, respectively, and determining an elliptical error region probability object having a center and a pair of axes containing the first and second signal data blocks. The center represents a highest probability location of a signal emitter at the second time and the pair of axes represents the spatial error of the center.